Technological advancements in digital medicine have brought improvements in the personalization of pharmaceutical products. Thus, AI is a key enabling technology in the context of the entire pharmaceutical product life cycle and can significantly improve the speed of product development, therapeutic efficacy, and product quality. This scholarly paper attempts a systematic review of the use of AI across the pharmaceutical value chain, by searching for articles in such databases as PubMed and IEEE Xplore. The initial search yielded 6131 articles, of which 73 (1. 2%) studies were included in the review based on the relevance of outcomes, publication type, and the availability of sufficient data. The systematic process of extracting data was in compliance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). AI systems in pharmaceuticals can be said to exist in different but intersecting implementation domains. Out of the 177 projects assessed, the AI use cases in clinical and pre-clinical trials occupied the highest proportion of 34%. Just behind are new small molecule design systems, which make up nearly one-third of the total. Target identification for novel medicines is the third most common application as seen with over 25% of the systems providing this feature. However, there is also a trend where 57% of developed AI systems are focused on one particular domain, with 102 systems being developed for only one particular domain. No single AI solution offers exhaustive coverage of all the lifecycle stages and tasks of stages and tasks. The above meta-analysis, performed in 2024 with considerable emphasis on India, demonstrates potential uses of AI in the pharmaceutical product life cycle although no single solution covering most of the functional areas appears to exist.